Time Over Matter: Measuring the Reasonableness of Officer Conduct in § 1983 Claims
Bibliographic record
Abstract
In the United States, far more police encounters result in civilian and officer deaths than in other democratic countries. When a government actor uses excessive force against an individual during an arrest or investigatory stop in violation of the Fourth Amendment right against unreasonable seizure, 42 U.S.C. § 1983 provides a federal civil remedy for that individual. In Graham v. Connor and Tennessee v. Garner, the U.S. Supreme Court held that courts should assess the reasonableness of an officer’s use of force to seize an individual in light of the “totality of the circumstances,” which includes the severity of the crime, whether the suspect actively resisted arrest, and whether the suspect posed a threat to the officers and bystanders. However, the Court has never delineated how lower courts should assess the totality of the circumstances in excessive force claims under § 1983. Thus, circuit courts have applied varying methods to analyze law enforcement’s use of force. This Note examines whether the Second Circuit’s narrow approach, the Third Circuit’s broad approach, or the Seventh Circuit’s segmented approach properly identifies the circumstances to consider when measuring the reasonableness of officers’ uses of force during a seizure in § 1983 claims. This Note compares the three circuit court approaches to how Canadian courts evaluate the reasonableness of police conduct in excessive force claims. Ultimately, this Note concludes that the Third Circuit’s approach, which considers causally relevant conduct, such as preseizure conduct, is truest to the notion of “totality” and should be the uniform method. As illustrated by Canadian courts, this Note argues that the Third Circuit standard incorporates de-escalation training as a factor in the reasonableness analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".